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Paper Citation Record · LEDGER

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts

As of 13 August 2026, this Paper Citation Record lists 100 of 149 outbound references and 0 inbound Pith citation observations for arXiv:2507.16946.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2507.16946 v1

Coverage vector

measured 100 of 149 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:06:24.744696Z

measured 100 of 100 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

100 of 149 outbound references displayed

  • verified exact5
  • verified fuzzy17
  • unresolved78
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 89ed104e-0305-4cdf-96d0-660613f13f83 · outbound

This paper cites CableInspect-AD: An expert- annotated anomaly detection dataset.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts CableInspect-AD: An expert- annotated anomaly detection dataset

Reference 1

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Observation d43d208f-cacb-4048-8c9e-aa5ccd256e81 · outbound

This paper cites Dual-path frequency discriminators for few-shot anomaly detection.Knowledge- Based Systems, 2024.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Dual-path frequency discriminators for few-shot anomaly detection.Knowledge- Based Systems, 2024

Reference 2

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Observation 4a1c38fd-9cf4-4c45-894f-e7ce1a350dc5 · outbound

This paper cites The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

Reference 3

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Observation bf241d17-0584-4289-ade4-a94ab95d21e3 · outbound

This paper cites BMAD: Benchmarks for med- ical anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts BMAD: Benchmarks for med- ical anomaly detection

Reference 4

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Observation c242c33d-caa3-4bcb-9a92-34cd841bb6eb · outbound

This paper cites MVTec AD–a comprehensive real-world dataset for unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MVTec AD–a comprehensive real-world dataset for unsupervised anomaly detection

Reference 5

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Observation aceefc85-d53f-4117-98ba-9b37b46e4a81 · outbound

This paper cites The liver tumor segmentation benchmark (lits).

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts The liver tumor segmentation benchmark (lits)

Reference 6

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Observation afd10c94-7f5b-4916-9bbf-1cebbcd3b111 · outbound

This paper cites Segment Any Anomaly without Training via Hybrid Prompt Regularization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Segment Any Anomaly without Training via Hybrid Prompt Regularization

Reference 7

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Observation 451ac339-7fed-44e0-b369-95bb97edd32f · outbound

This paper cites A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect

Reference 8

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Observation 32793536-dfba-45ad-ad8c-8b19c2869a8c · outbound

This paper cites AdaCLIP: Adapt- ing CLIP with hybrid learnable prompts for zero-shot anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AdaCLIP: Adapt- ing CLIP with hybrid learnable prompts for zero-shot anomaly detection

Reference 9

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Observation 2cdc66dd-64b9-44d4-9763-0194751a339a · outbound

This paper cites Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Human-Free Automated Prompting for Vision-Language Anomaly Detection: Prompt Optimization with Meta-guiding Prompt Scheme

Reference 10

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Source-reported events for the cited work

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Observation 4d108bb2-d6a7-42c8-83a7-6a1aa245c268 · outbound

This paper cites A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A unified anomaly synthesis strategy with gradient ascent for industrial anomaly detection and localization

Reference 11

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Observation 503cec6b-8f00-42fb-a66e-2e2239624cee · outbound

This paper cites Sub-Image Anomaly Detection with Deep Pyramid Correspondences.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Sub-Image Anomaly Detection with Deep Pyramid Correspondences

Reference 12

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Observation 8a4daed3-a86e-4337-88b6-e14cf1808bb1 · outbound

This paper cites Microsoft Copilot, 2023.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Microsoft Copilot, 2023

Reference 13

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Observation 85145034-1843-41f0-9919-d95f3de7fa8a · outbound

This paper cites PaDiM: a patch distribution modeling framework for anomaly detection and localization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts PaDiM: a patch distribution modeling framework for anomaly detection and localization

Reference 14

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Observation 3b14db45-d729-4dc0-a132-8e7ddbdb161b · outbound

This paper cites Anomaly detection via reverse distillation from one-class embedding.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Anomaly detection via reverse distillation from one-class embedding

Reference 15

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Observation b7a70748-717a-42d9-9e3e-cf8014e2213a · outbound

This paper cites Continual learning for anomaly detection in surveillance videos.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Continual learning for anomaly detection in surveillance videos

Reference 16

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Observation 5bb3771f-d3ed-4873-b3a3-f45585defd37 · outbound

This paper cites Transformers: Align model docu- mentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Transformers: Align model docu- mentation

Reference 17

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Observation 6e24a02c-66af-4c75-a961-f89d8e8568f3 · outbound

This paper cites ChangeChip: A reference-based unsupervised change de- tection for PCB defect detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ChangeChip: A reference-based unsupervised change de- tection for PCB defect detection

Reference 18

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Observation 3050249a-4db8-459e-b35e-0c7786763d72 · outbound

This paper cites TransFusion–a transparency-based diffusion model for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts TransFusion–a transparency-based diffusion model for anomaly detection

Reference 19

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Observation 47a3b7fa-49f4-4940-a097-f2a9811b3c0f · outbound

This paper cites Leveraging vector-quantized variational autoencoder inner metrics for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Leveraging vector-quantized variational autoencoder inner metrics for anomaly detection

Reference 20

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Observation e9a18568-99bd-4b21-aa27-09c62c38a58d · outbound

This paper cites Learning to detect multi-class anomalies with just one normal image prompt.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learning to detect multi-class anomalies with just one normal image prompt

Reference 21

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Observation 555185e9-f467-40c2-b9e0-feb38847393d · outbound

This paper cites Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Towards Total Online Unsupervised Anomaly Detection and Localization in Industrial Vision

Reference 22

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Observation b8245ceb-9518-435e-ae7f-e9f7be8dde1f · outbound

This paper cites Real-time evaluation in online continual learning: A new hope.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Real-time evaluation in online continual learning: A new hope

Reference 23

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Observation de0432bd-4cab-4ffb-8e9b-06580ccc4a83 · outbound

This paper cites Filo: Zero-shot anomaly detection by fine-grained description and high- quality localization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Filo: Zero-shot anomaly detection by fine-grained description and high- quality localization

Reference 24

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Observation d14a1a43-1116-4539-bd66-a58b9980b8ca · outbound

This paper cites AnomalyGPT: Detecting in- dustrial anomalies using large vision-language models.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AnomalyGPT: Detecting in- dustrial anomalies using large vision-language models

Reference 25

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Observation 6342e330-054b-4d99-b355-abcb219fa22b · outbound

This paper cites Few-shot anomaly-driven generation for anomaly classification and segmentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Few-shot anomaly-driven generation for anomaly classification and segmentation

Reference 26

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Observation 8fb96994-77bb-4e2e-ab98-3b326cabc2e2 · outbound

This paper cites Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Absolute-Unified Multi-Class Anomaly Detection via Class-Agnostic Distribution Alignment

Reference 27

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Observation 2c1dcd65-2da6-4b91-aa9c-7bb2d0c41ef9 · outbound

This paper cites Br35H: Brain tumor detection 2020,.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Br35H: Brain tumor detection 2020,

Reference 28

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Observation b7c29ef9-8995-4a89-b60b-1a7509978757 · outbound

This paper cites OneLLM: One framework to align all modali- ties with language.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts OneLLM: One framework to align all modali- ties with language

Reference 29

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Observation 97945ddd-cb55-4a0f-99fa-c96cc6f28a39 · outbound

This paper cites MambaAD: Exploring state space models for multi-class unsupervised anomaly detec- tion.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MambaAD: Exploring state space models for multi-class unsupervised anomaly detec- tion

Reference 30

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Observation cbc2afdd-eac9-44fa-a476-a46fad0b36a4 · outbound

This paper cites A diffusion-based framework for multi-class anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A diffusion-based framework for multi-class anomaly detection

Reference 31

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Observation 2b05d1f6-21ef-4896-9098-80618a619c91 · outbound

This paper cites Deep residual learning for image recognition.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Deep residual learning for image recognition

Reference 32

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Observation 9f88ccc1-935b-41b8-be92-8a29791cfa55 · outbound

This paper cites Learning unified reference rep- resentation for unsupervised multi-class anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learning unified reference rep- resentation for unsupervised multi-class anomaly detection

Reference 33

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Observation b07a383f-ef73-4fa5-ae50-90eade01e1a3 · outbound

This paper cites Long-tailed anomaly detection with learnable class names.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Long-tailed anomaly detection with learnable class names

Reference 34

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Observation aa015249-eb91-4121-8a05-2fc6fddf56d2 · outbound

This paper cites Automated seg- mentation of macular edema in oct using deep neural net- works.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Automated seg- mentation of macular edema in oct using deep neural net- works

Reference 35

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Observation dbb625ac-6bc9-4800-b5b7-236d66281da6 · outbound

This paper cites Registration based few-shot anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Registration based few-shot anomaly detection

Reference 36

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source=pdf_text observed=2026-08-06T15:06:24.502923Z digest=sha256:7b15fb0b3d002978bb3b1156c0f3980adc588d81e115cda1b6e3187150e2f76f

Observation 6e69525d-a94d-4937-9815-da80bb436461 · outbound

This paper cites Adapting visual- language models for generalizable anomaly detection in medical images.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Adapting visual- language models for generalizable anomaly detection in medical images

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Observation 7f8665c5-327c-4cd9-ac96-b556529b95cb · outbound

This paper cites ReCon- Patch: Contrastive patch representation learning for indus- trial anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ReCon- Patch: Contrastive patch representation learning for indus- trial anomaly detection

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source=pdf_text observed=2026-08-06T15:06:24.510195Z digest=sha256:710800840003be5803ff94d304e6b6625d3f8c163675c2d573abf37681ed0793

Observation ee3b911d-cefb-427c-b6ad-f7bb84690d3a · outbound

This paper cites Towards open-world object-based anomaly detection via self-supervised outlier synthesis.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Towards open-world object-based anomaly detection via self-supervised outlier synthesis

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source=pdf_text observed=2026-08-06T15:06:24.513734Z digest=sha256:39d0c79c4afa149c125aee079f5962c342dab92b3953f8253e91f05efe066185

Observation 6d240a20-fbb6-4bec-8ee7-9e33bf18ccc7 · outbound

This paper cites WinCLIP: Zero- /few-shot anomaly classification and segmentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts WinCLIP: Zero- /few-shot anomaly classification and segmentation

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source=pdf_text observed=2026-08-06T15:06:24.517398Z digest=sha256:589aea29199a5ff46308b9e3303a95b009bcb1ec0af859685d4849e32d0e8125

Observation 4ac40283-b55e-404d-bc32-8387862c7dce · outbound

This paper cites Scaling up visual and vision-language representation learning with noisy text supervision.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Scaling up visual and vision-language representation learning with noisy text supervision

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source=pdf_text observed=2026-08-06T15:06:24.520860Z digest=sha256:46564b7a29e6c36ca3f7727779d6ded574e6c77f002efe7a9a27050b66e12047

Observation e35a8911-e4eb-4641-aea2-b3bdd47ab346 · outbound

This paper cites Brain tumor detec- tion using MRI images.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Brain tumor detec- tion using MRI images

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source=pdf_text observed=2026-08-06T15:06:24.524760Z digest=sha256:7e5b487de13aafeff1b73ae9203ffde160c1c6484c78d21e271d2fafc1a7653c

Observation f2175444-14c0-4e86-966b-93ec3ecf6e9a · outbound

This paper cites Head CT - hemorrhage, 2018.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Head CT - hemorrhage, 2018

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source=pdf_text observed=2026-08-06T15:06:24.528493Z digest=sha256:b06ffd5f846616efdb98420b04d94928e5eff5cbb0f9eced9c1635fb242bfae5

Observation bf5c8173-1a31-4dcd-8707-b2d6e6d9030b · outbound

This paper cites Online continual learning on class incremental blurry task configuration with anytime inference.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Online continual learning on class incremental blurry task configuration with anytime inference

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source=pdf_text observed=2026-08-06T15:06:24.532150Z digest=sha256:d0542f2f9c8456b9d6443a5a623dad2f0fcca59f3e509d53b647882378d4587c

Observation 4b0040c4-0510-41e1-a1ac-817a90d11ac7 · outbound

This paper cites Miccai multi-atlas la- beling beyond the cranial vault–workshop and challenge.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Miccai multi-atlas la- beling beyond the cranial vault–workshop and challenge

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source=pdf_text observed=2026-08-06T15:06:24.536061Z digest=sha256:0c4418fb78c66f3c6a1ce609a4e252c6293f18c93ef4acad34cefbfaa0c98c15

Observation 190094b2-d114-41a2-800e-288ea761bf3f · outbound

This paper cites Continuous memory representation for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Continuous memory representation for anomaly detection

Reference 46

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source=pdf_text observed=2026-08-06T15:06:24.539755Z digest=sha256:dc3c879071552a5f5726664834f494fe2c43e3ff6da9cf20f631e45715ab7ca3

Observation 70898475-a245-4b49-b58e-bdc4cf289caf · outbound

This paper cites AD3: Introducing a score for anomaly detec- tion dataset difficulty assessment using VIADUCT dataset.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AD3: Introducing a score for anomaly detec- tion dataset difficulty assessment using VIADUCT dataset

Reference 47

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source=pdf_text observed=2026-08-06T15:06:24.543503Z digest=sha256:bd3c818121caa38829215ad92bc30c216770b7a5524209e80b8510b5253f8400

Observation 9fcab1f9-b02b-4de5-b1eb-a95979a8a64e · outbound

This paper cites CutPaste: Self-supervised learning for anomaly de- tection and localization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts CutPaste: Self-supervised learning for anomaly de- tection and localization

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source=pdf_text observed=2026-08-06T15:06:24.547200Z digest=sha256:1862142589e1bb25cad0c2644f015c45f247b90ce65e575b87a13ff06793612a

Observation 4cd212ae-a602-4c7a-a7c1-022a765caddc · outbound

This paper cites ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ClipSAM: CLIP and SAM Collaboration for Zero-Shot Anomaly Segmentation

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local_arxiv, observed 2026-08-06T15:06:25.635748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.550689Z digest=sha256:9be8906663e32e3e5660db2c5636385f2d28b8b566f587f4a44fc3d312b19ed3

Observation 51dc7f09-5250-42cb-8269-a5fecfdd835e · outbound

This paper cites MuSc: Zero-shot industrial anomaly classification and segmenta- tion with mutual scoring of the unlabeled images.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MuSc: Zero-shot industrial anomaly classification and segmenta- tion with mutual scoring of the unlabeled images

Reference 50

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source=pdf_text observed=2026-08-06T15:06:24.554839Z digest=sha256:ec9e4613a8cb606611e45b9906c9baf32e2c5753aec7c28fc1d8bedff993faf9

Observation 698df711-218b-48dd-92d6-7028b94e0ead · outbound

This paper cites PromptAD: Learning prompts with only normal samples for few-shot anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts PromptAD: Learning prompts with only normal samples for few-shot anomaly detection

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source=pdf_text observed=2026-08-06T15:06:24.558392Z digest=sha256:fb07d65cc6d7fa3930c776e2054dc8458684329046d523a974be69a4977fce35

Observation 193aeb56-51c2-4abe-b8ce-0b34a102960e · outbound

This paper cites FADE: Few-shot/zero-shot anomaly detection engine using large vision-language model.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts FADE: Few-shot/zero-shot anomaly detection engine using large vision-language model

Reference 52

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source=pdf_text observed=2026-08-06T15:06:24.562223Z digest=sha256:5a9bbb4ab59c55ad3e963ab70df858abb8fc17971ad8d680a54d3c329ab3a815

Observation 60855bd5-2301-42e5-af0c-b471b8258311 · outbound

This paper cites COFT-AD: Contrastive fine-tuning for few-shot anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts COFT-AD: Contrastive fine-tuning for few-shot anomaly detection

Reference 53

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source=pdf_text observed=2026-08-06T15:06:24.565803Z digest=sha256:129c6ce79241eafcdcb9d56d2cc6a8867bd53deba8740bd86ae9ae95810824c6

Observation 40450dee-7bb2-4dff-959c-192d088eca06 · outbound

This paper cites Learning diffusion models for multi-view anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learning diffusion models for multi-view anomaly detection

Reference 54

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source=pdf_text observed=2026-08-06T15:06:24.569585Z digest=sha256:ad9a2ff0ed719b3fba3ce4b6d2bb3a3a1c26ee75b2b17b1a2fb4da377d3dd3c5

Observation 56850e83-34e9-49cb-8b86-2f93fb5e5faf · outbound

This paper cites Unsupervised continual anomaly detection with contrastively-learned prompt.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Unsupervised continual anomaly detection with contrastively-learned prompt

Reference 55

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source=pdf_text observed=2026-08-06T15:06:24.573196Z digest=sha256:f43affed32addd2510bd5f0935153e345906e124a52511496769f240305e33e8

Observation fe7049e8-4ae4-40cf-8e31-b5b510a601da · outbound

This paper cites Heterogeneity-aware recurrent neu- ral network for hyperspectral and multispectral image fu- sion.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Heterogeneity-aware recurrent neu- ral network for hyperspectral and multispectral image fu- sion

Reference 56

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source=pdf_text observed=2026-08-06T15:06:24.576650Z digest=sha256:459d48cc4ff0f8a467ab3ce04ae5b7981f6c43fd009cae81e1d0305e13bee677

Observation 88126336-5d9f-4af2-bd06-a5e2c73f36ef · outbound

This paper cites Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Hierarchical vector quantized transformer for multi-class unsupervised anomaly detection

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source=pdf_text observed=2026-08-06T15:06:24.580781Z digest=sha256:cfca54786dfcab7b5f0cdee917abbea08669f2366eea9903fc32a50da84eef82

Observation 638e5ed3-4e73-4cf2-ab0c-4bf618da64d6 · outbound

This paper cites Review of wafer surface defect detection methods.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Review of wafer surface defect detection methods

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source=pdf_text observed=2026-08-06T15:06:24.585045Z digest=sha256:b7d5f5f3652ea4d681c2fd9055e3dd5723e52b9738b74364c66d41ef97026242

Observation 2bdf8618-188a-4768-8b48-a58766695824 · outbound

This paper cites Anomaly de- tection through latent space restoration using vector quan- tized variational autoencoders.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Anomaly de- tection through latent space restoration using vector quan- tized variational autoencoders

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source=pdf_text observed=2026-08-06T15:06:24.588857Z digest=sha256:d85526bcd6ac90ab5622988c5197786fda124b89a84f8885e63fe95ec71972f7

Observation 373891c2-88b9-476d-8a91-be5db03598c3 · outbound

This paper cites Mixture of ex- perts: a literature survey.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Mixture of ex- perts: a literature survey

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source=pdf_text observed=2026-08-06T15:06:24.592646Z digest=sha256:268c27f65c8e89e9910a27c0b80868c02d1e32f5f017244eb84fd9aba9b64484

Observation 4a2b1682-28fa-4b22-a64e-8169aff7e476 · outbound

This paper cites Unsu- pervised, online and on-the-fly anomaly detection for non- stationary image distributions.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Unsu- pervised, online and on-the-fly anomaly detection for non- stationary image distributions

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source=pdf_text observed=2026-08-06T15:06:24.596489Z digest=sha256:896c2e46fa1604112a585943b3074a10f473e6408da88fe3a7fb99f7423192ac

Observation d748cb8e-9d20-4514-99b6-844d3cab02b7 · outbound

This paper cites MoEAD: A parameter- efficient model for multi-class anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts MoEAD: A parameter- efficient model for multi-class anomaly detection

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source=pdf_text observed=2026-08-06T15:06:24.599962Z digest=sha256:7b58066c89f99f7f4f6a693832fc2f7f298756bb6cacb4bda244a23c37979ede

Observation 6a5e2b89-15f5-477e-9c31-478c1dc2c601 · outbound

This paper cites RGI: Robust GAN- inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts RGI: Robust GAN- inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection

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source=pdf_text observed=2026-08-06T15:06:24.603470Z digest=sha256:86ac5cd96aac3853a13c8e6f889416da18cf48943c2f84bcf73ab3926fc42001

Observation a9737e6d-38bf-4bf7-873d-fb3f7926d7fc · outbound

This paper cites ChatGPT, 2023.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts ChatGPT, 2023

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source=pdf_text observed=2026-08-06T15:06:24.607132Z digest=sha256:3511329555afade7f438a2507bd101924becba9bd1759b6d29d4c95319fcdd53

Observation 464cd2ed-d535-4fda-b5b7-4002c7d16df0 · outbound

This paper cites Deep learning for anomaly detection: A review.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Deep learning for anomaly detection: A review

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source=pdf_text observed=2026-08-06T15:06:24.610853Z digest=sha256:913f9a8bdd755eb1b91e0c761449333fb9109c51d9383e413fb1cba98b0d66c6

Observation 5f735a8b-1922-4eb7-bd6f-fec1987d3dcd · outbound

This paper cites Revisiting deep feature reconstruction for logical and structural industrial anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Revisiting deep feature reconstruction for logical and structural industrial anomaly detection

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source=pdf_text observed=2026-08-06T15:06:24.614559Z digest=sha256:331f24718f2363ad890ebb051ad905786550d96fa933bc5b04348183a533fb07

Observation afde420c-4e41-409d-a5c0-0c7c9dc10093 · outbound

This paper cites VCP-CLIP: A visual context prompting model for zero-shot anomaly seg- mentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts VCP-CLIP: A visual context prompting model for zero-shot anomaly seg- mentation

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source=pdf_text observed=2026-08-06T15:06:24.618357Z digest=sha256:776591fa17208796da98f01f4ba87599ac54c80b61467569ec497e171f2e0a29

Observation f6080dc1-9aea-4642-a309-461fa745f657 · outbound

This paper cites Learn- ing transferable visual models from natural language super- vision.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Learn- ing transferable visual models from natural language super- vision

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source=pdf_text observed=2026-08-06T15:06:24.621825Z digest=sha256:099a481c3e9c99a60b875b017bca3018a1a288e1eb48d9aee7f2bbed001ba608

Observation cfb28705-0a58-4b08-a2f5-1827708d6023 · outbound

This paper cites DELTA: Decoupling long-tailed online continual learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts DELTA: Decoupling long-tailed online continual learning

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source=pdf_text observed=2026-08-06T15:06:24.625581Z digest=sha256:c37fd4f4c95a54984386bf5cc395afc5d0179aadb123e2cb2f41ebfa2f075a2e

Observation 94c3faad-2152-4b83-82fa-d7445fd434cf · outbound

This paper cites Variational infer- ence with normalizing flows.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Variational infer- ence with normalizing flows

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source=pdf_text observed=2026-08-06T15:06:24.629473Z digest=sha256:98eabb2495f30e4b01d4dc34907e2f56dcf5cdab6bf6a6e2e6449b4adba8ba36

Observation 6089365d-f7e4-4ddd-8d60-20c601852cc5 · outbound

This paper cites Scaling vision with sparse mix- ture of experts.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Scaling vision with sparse mix- ture of experts

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source=pdf_text observed=2026-08-06T15:06:24.633385Z digest=sha256:ce7be12bbb8a1f8f2a96e010cd984d5dd58d9ca789c9445c51e3ab619fedfc87

Observation 8904cecb-f9a9-48f0-b310-39c8e69cc7f9 · outbound

This paper cites Towards to- tal recall in industrial anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Towards to- tal recall in industrial anomaly detection

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source=pdf_text observed=2026-08-06T15:06:24.636974Z digest=sha256:58f4b09b917e5b5aa1f083ae7a683ab1e8bbbd436a23014cc8dab9dbe616dd3f

Observation eb5270cd-5d2a-4998-a3b4-0a4b9cd2fd9d · outbound

This paper cites Same same but differnet: Semi-supervised defect detection with normalizing flows.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Same same but differnet: Semi-supervised defect detection with normalizing flows

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source=pdf_text observed=2026-08-06T15:06:24.641981Z digest=sha256:d8fca6aa80c14a730689d661dd7602aa39c25ab76f00ab3faead6d403145dc46

Observation 63c0a64f-c579-4130-b744-e51efc48dfd4 · outbound

This paper cites Tire defect detection model using ma- chine learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Tire defect detection model using ma- chine learning

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source=pdf_text observed=2026-08-06T15:06:24.646009Z digest=sha256:a3f7a2470c86978f4505ee48134718ab612c7acf0bca0fcd08f11116151a272b

Observation 12692ee8-cee4-48b2-bf9b-97f7bc7a8535 · outbound

This paper cites Multiresolution knowledge distillation for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Multiresolution knowledge distillation for anomaly detection

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:24.649967Z digest=sha256:c4e80cb06728b107f6b84feadd4fccd6418dc627058c142784a425842ba10ec8

Observation 6b0c17c8-9f19-49d3-9605-7fb9d2d53612 · outbound

This paper cites Dissolving is amplifying: Towards fine-grained anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Dissolving is amplifying: Towards fine-grained anomaly detection

Reference 76

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:24.653565Z digest=sha256:7cc634f64c3ea73ba459d3e182406fd92bcfda31932d8bfb2f08da920aa8849c

Observation f2522519-ba90-4a90-9bbd-ac6671079c98 · outbound

This paper cites GeneralAD: Anomaly detection across domains by attending to distorted features.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts GeneralAD: Anomaly detection across domains by attending to distorted features

Reference 77

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:24.657036Z digest=sha256:74345aa6857aef80171a036e665b0693641cded5efcabd60fff64f03ca30ec30

Observation 189ce715-bd63-4e2a-b807-cd8164e1e6d9 · outbound

This paper cites EfficientNet: Rethinking model scaling for convolutional neural networks.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts EfficientNet: Rethinking model scaling for convolutional neural networks

Reference 78

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.526721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.660464Z digest=sha256:9a7b28ef6c1318b172d180385a468e45047d547b502ddb310737dd142fae2884

Observation e819af4c-df5a-4ca4-a775-f78c9b635757 · outbound

This paper cites An incremental unified framework for small defect inspection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts An incremental unified framework for small defect inspection

Reference 79

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.513965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.664065Z digest=sha256:55a725d751934d99504196d7a0195dd6968b479e963554213836a66921ae7bb8

Observation 2bdcb882-d38a-464b-b3d9-75c6cb922543 · outbound

This paper cites Revisiting reverse distillation for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Revisiting reverse distillation for anomaly detection

Reference 80

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.501823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.667749Z digest=sha256:2331fe208dbcbc16a9bf3f9ce756dc6a5cecb06f9d045a69422faeb12782c5b7

Observation bed54cc2-5684-4b32-a7ef-9a9cb5685342 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts LLaMA: Open and Efficient Foundation Language Models

Reference 81

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-06T15:06:24.671346Z digest=sha256:b4eaa9e2432e07018224626ef32f261be527162a5b2a22ffe0fea0c11ef57266

Observation 811b869b-d90b-4ae0-993c-ee82341552ea · outbound

This paper cites Neural discrete representation learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Neural discrete representation learning

Reference 82

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.489366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.675497Z digest=sha256:4b64d7eac4a046170bacedd94302de2c6c0e6476c204080f08c66206b9621574

Observation e9ce8263-a7af-412a-8c49-c28f0b45b219 · outbound

This paper cites A comprehensive survey of continual learning: theory, method and application.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A comprehensive survey of continual learning: theory, method and application

Reference 83

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.477211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.679442Z digest=sha256:811071c83198721556b58b3a3566345b53f3f7ce6f51bdc2ce17840a464ac0ba

Observation 63b25924-bbfe-4f5f-8394-97ba11b82302 · outbound

This paper cites Few-shot online anomaly detection and segmentation.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Few-shot online anomaly detection and segmentation

Reference 84

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.465306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.683179Z digest=sha256:90243bc9d55e9f44a840573e8ce41a5a28b87ee7fed2fe75e7bde1c6c87e1a22

Observation 387660d0-de35-400e-9294-1be207535855 · outbound

This paper cites Weakly supervised learning for industrial optical inspection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Weakly supervised learning for industrial optical inspection

Reference 85

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raw_fallback, observed 2026-08-06T15:06:26.453472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.686992Z digest=sha256:442cb5a5986c96b85f224502a3b8fd2fef5ca211bac25adf350fb43f798ecf6b

Observation 46641ebb-fe93-4154-aa2b-6473ba5b20fb · outbound

This paper cites Defect spectrum: A granular look of large-scale defect datasets with rich seman- tics.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Defect spectrum: A granular look of large-scale defect datasets with rich seman- tics

Reference 86

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.441392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.690809Z digest=sha256:39b11b9f9468044e4fb96fd4aaf3f221ebcec297bdd4f88c869b3e5ed63f8fb7

Observation e312f0fc-6f39-4cb2-88f1-37c271146609 · outbound

This paper cites GLAD: Towards better reconstruction with global and local adaptive diffu- sion models for unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts GLAD: Towards better reconstruction with global and local adaptive diffu- sion models for unsupervised anomaly detection

Reference 87

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.429362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.694848Z digest=sha256:43014bf3d8d96156367a54ba8aea7b97424c6c286dcfa4b7df73d51bc217ce32

Observation 1c3d783f-68a1-4360-8be6-27942f5cfa21 · outbound

This paper cites Hierarchical gaussian mixture normal- izing flow modeling for unified anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Hierarchical gaussian mixture normal- izing flow modeling for unified anomaly detection

Reference 88

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verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.416664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.698371Z digest=sha256:2202450c999d7052d79989befc1a8647235eb40baaad5693dd26584c99a179ac

Observation 703c86b2-e0be-4eae-8bbb-a732184f2cca · outbound

This paper cites A unified model for multi-class anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A unified model for multi-class anomaly detection

Reference 89

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.404415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.702520Z digest=sha256:545d74eecb961e5bee2823aa0e27323168fce0af00c030e1841b00bd0770d9ec

Observation 061896e5-9e47-4e27-89b5-f950a7bb6271 · outbound

This paper cites FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing Flows

Reference 90

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unresolved
no resolver link, observed 2026-08-06T15:06:24.706782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:24.706782Z digest=sha256:622e37c4f2aee158d668a647b66e2d7381621868a70a21c65c4cdb94c3c6d845

Observation 6f0d3926-16bd-455f-9885-cfe50a27a3bb · outbound

This paper cites DRAEM-a discriminatively trained reconstruction embed- ding for surface anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts DRAEM-a discriminatively trained reconstruction embed- ding for surface anomaly detection

Reference 91

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.392444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.710922Z digest=sha256:18530883bd5d9d11f45f04968c54532354ba1cab4d617fba3d860adc2959d805

Observation 4e9f144e-9b86-4abb-944c-756f8b44e15e · outbound

This paper cites A Systematic Review on Long-Tailed Learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A Systematic Review on Long-Tailed Learning

Reference 92

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verified exact
local_arxiv, observed 2026-08-06T15:06:25.589547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.714627Z digest=sha256:b4fe6c0aa4eb247c026248b2d1dfe1fe6225c99c40c59e5aac30c2c99c848a4b

Observation 3dac0a2e-8d31-41ba-a71f-d72372582570 · outbound

This paper cites Defect-GAN: High-fidelity defect synthesis for automated defect inspection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Defect-GAN: High-fidelity defect synthesis for automated defect inspection

Reference 93

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.379292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.718635Z digest=sha256:a3321a76abae138f0d73b1e4bdf0041d0dde361f47f3dac8295af83e73324b00

Observation a4eb71bd-a62d-4ee2-b4c5-b66959740f73 · outbound

This paper cites GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts GPT-4V-AD: Exploring Grounding Potential of VQA-oriented GPT-4V for Zero-shot Anomaly Detection

Reference 94

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:24.722351Z digest=sha256:46d719eb36850805f2647cbc656bed9e8e7d22d8f8ac6a3be62a380a2254c9d5

Observation 4a689aea-0531-400c-9e58-ac0bfbec23b2 · outbound

This paper cites Exploring plain ViT reconstruction for multi- class unsupervised anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Exploring plain ViT reconstruction for multi- class unsupervised anomaly detection

Reference 95

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.366583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.726117Z digest=sha256:2de2e306629381fccb4b5113d1d906c70cabfd0a7500a996f73b9b794468eb7b

Observation b45fcf4b-96d8-45ff-aeb4-f6812680cb62 · outbound

This paper cites A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection

Reference 96

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unresolved
no resolver link, observed 2026-08-06T15:06:24.729744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:24.729744Z digest=sha256:955f179a24614e7aca15a21f24fc15674f1292661eb8a2c1b22e7b9724a50f58

Observation efbe44d8-c251-4902-b2c0-be0848f55ff9 · outbound

This paper cites DeSTSeg: Segmentation guided denois- ing student-teacher for anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts DeSTSeg: Segmentation guided denois- ing student-teacher for anomaly detection

Reference 97

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.354201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.733439Z digest=sha256:ca70e83155004c762a6ccc1eeb770eb9a4e8fa44692efb757a9e03796e1df0e8

Observation a81cb44e-c014-4261-ba5b-b64825c1889b · outbound

This paper cites Meta-Transformer: A Unified Framework for Multimodal Learning.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts Meta-Transformer: A Unified Framework for Multimodal Learning

Reference 98

Resolution
unresolved
no resolver link, observed 2026-08-06T15:06:24.737211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T15:06:24.737211Z digest=sha256:3d5f35c42c325102051e60dd8d7968b001c90b9e2e4442155cc72151d6af915a

Observation 1f8eba74-3889-4dc3-8fde-eda6fc48f0c3 · outbound

This paper cites OmniAL: A unified cnn framework for unsu- pervised anomaly localization.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts OmniAL: A unified cnn framework for unsu- pervised anomaly localization

Reference 99

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.342179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.740958Z digest=sha256:e952e2678d94fd6040e29846235a86810339b449901d279d1dd9b1e9d9db50fd

Observation da5e308c-ffc4-48de-98ab-0d3bf8da8314 · outbound

This paper cites AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection.

Toward Long-Tailed Online Anomaly Detection through Class-Agnostic Concepts AnomalyCLIP: Object-agnostic prompt learning for zero-shot anomaly detection

Reference 100

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T15:06:26.330123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-08-06T15:06:24.744696Z digest=sha256:bad3a44f674a13ec29d3e19f5c69aa56f3fd914328d7d634d9e43aa71ba17973

Pith citing papers

No inbound Pith citation observations are available.